


import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


SCREAMING_SNAKE_CASE						:Optional[int]			=  logging.get_logger(__name__)



def       _lowerCAmelCase    (				lowerCAmelCase_				:List[str]=None		,    lowerCAmelCase_				:Optional[Any]=None				)->Optional[Any]:
   '''simple docstring'''






   return field(default_factory=lambda: default		,    metadata=_lowercase				)



@dataclass
class      __lowerCAmelCase  :






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							list_field(
      default=[]			,	metadata={
          'help': (
              'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
              ' of all available models'
          )
      }			,	)

  _SCREAMING_SNAKE_CASE		     =							list_field(
      default=[8]			,	metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'}       )

  _SCREAMING_SNAKE_CASE		     =							list_field(
      default=[8, 32, 128, 512]			,	metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}			,	)

  _SCREAMING_SNAKE_CASE		     =							field(
      default=__snake_case			,	metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}			,	)
  _SCREAMING_SNAKE_CASE		     =							field(
      default=__snake_case			,	metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}			,	)
  _SCREAMING_SNAKE_CASE		     =							field(
      default=__snake_case			,	metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}       )
  _SCREAMING_SNAKE_CASE		     =							field(default=__snake_case			,	metadata={'help': 'Use FP16 to accelerate inference.'}       )
  _SCREAMING_SNAKE_CASE		     =							field(default=__snake_case			,	metadata={'help': 'Benchmark training of model'}       )
  _SCREAMING_SNAKE_CASE		     =							field(default=__snake_case			,	metadata={'help': 'Verbose memory tracing'}       )
  _SCREAMING_SNAKE_CASE		     =							field(
      default=__snake_case			,	metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}			,	)
  _SCREAMING_SNAKE_CASE		     =							field(
      default=__snake_case			,	metadata={
          'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
      }			,	)
  _SCREAMING_SNAKE_CASE		     =							field(default=__snake_case			,	metadata={'help': 'Trace memory line by line'}       )
  _SCREAMING_SNAKE_CASE		     =							field(default=__snake_case			,	metadata={'help': 'Save result to a CSV file'}       )
  _SCREAMING_SNAKE_CASE		     =							field(default=__snake_case			,	metadata={'help': 'Save all print statements in a log file'}       )
  _SCREAMING_SNAKE_CASE		     =							field(default=__snake_case			,	metadata={'help': 'Whether to print environment information'}       )
  _SCREAMING_SNAKE_CASE		     =							field(
      default=__snake_case			,	metadata={
          'help': (
              'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
              ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
              ' for debugging / testing and on TPU.'
          )
      }			,	)
  _SCREAMING_SNAKE_CASE		     =							field(
      default=f"""inference_time_{round(time()       )}.csv"""			,	metadata={'help': 'CSV filename used if saving time results to csv.'}			,	)
  _SCREAMING_SNAKE_CASE		     =							field(
      default=f"""inference_memory_{round(time()       )}.csv"""			,	metadata={'help': 'CSV filename used if saving memory results to csv.'}			,	)
  _SCREAMING_SNAKE_CASE		     =							field(
      default=f"""train_time_{round(time()       )}.csv"""			,	metadata={'help': 'CSV filename used if saving time results to csv for training.'}			,	)
  _SCREAMING_SNAKE_CASE		     =							field(
      default=f"""train_memory_{round(time()       )}.csv"""			,	metadata={'help': 'CSV filename used if saving memory results to csv for training.'}			,	)
  _SCREAMING_SNAKE_CASE		     =							field(
      default=f"""env_info_{round(time()       )}.csv"""			,	metadata={'help': 'CSV filename used if saving environment information.'}			,	)
  _SCREAMING_SNAKE_CASE		     =							field(
      default=f"""log_{round(time()       )}.csv"""			,	metadata={'help': 'Log filename used if print statements are saved in log.'}			,	)
  _SCREAMING_SNAKE_CASE		     =							field(default=3			,	metadata={'help': 'Times an experiment will be run.'}       )
  _SCREAMING_SNAKE_CASE		     =							field(
      default=__snake_case			,	metadata={
          'help': (
              'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
              ' model weights.'
          )
      }			,	)



  def        lowerCAmelCase__	(  self							:							int	) ->			List[str]:




     """simple docstring"""
     warnings.warn(
         F'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
         " are deprecated in general and it is advised to use external Benchmarking libraries "
         " to benchmark Transformer models."     ,					_lowerCAmelCase     ,					)



  def        lowerCAmelCase__	(  self							:							List[str]	) ->			Union[str, Any]:




     """simple docstring"""
     return json.dumps(dataclasses.asdict(self	)     ,					indent=2	)



  @property
  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			str:




     """simple docstring"""






     if len(self.models	) <= 0:
        raise ValueError(
            "Please make sure you provide at least one model name / model identifier, *e.g.* `--models"
            " bert-base-cased` or `args.models = [\'bert-base-cased\']."	)
     return self.models



  @property
  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			Optional[int]:




     """simple docstring"""






     if not self.multi_process:
        return False
     elif self.is_tpu:
        logger.info("Multiprocessing is currently not possible on TPU."	)
        return False
     else:
        return True








"""simple docstring"""



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
		raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
		raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
lowercase_							=							logging.get_logger(__name__)

lowercase_							=							'Hello, World!'
lowercase_							=							'en_XX'
def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   str			,   _lowercase :   bool	)						-> Optional[int]:


	"""simple docstring"""




	lowerCAmelCase_      							=			Path('''data_bin'''	)
	lowerCAmelCase_      							=			FairseqXmodModel.from_pretrained(
	    model_name_or_path=str(Path(_lowercase	).parent	)			,   checkpoint_file=Path(_lowercase	).name			,   _name='''xmod_base'''			,   arch='''xmod_base'''			,   task='''multilingual_masked_lm'''			,   data_name_or_path=str(_lowercase	)			,   bpe='''sentencepiece'''			,   sentencepiece_model=str(Path(_lowercase	).parent / '''sentencepiece.bpe.model'''	)			,   src_dict=str(data_dir / '''dict.txt'''	)			,   )
	xmod.eval()  # disable dropout
	print(_lowercase	)

	lowerCAmelCase_      							=			xmod.model.encoder.sentence_encoder
	lowerCAmelCase_      							=			XmodConfig(
	    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings			,   hidden_size=xmod.cfg.model.encoder_embed_dim			,   num_hidden_layers=xmod.cfg.model.encoder_layers			,   num_attention_heads=xmod.cfg.model.encoder_attention_heads			,   intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim			,   max_position_embeddings=5_1_4			,   type_vocab_size=1			,   layer_norm_eps=1E-5			,   pre_norm=xmod.cfg.model.encoder_normalize_before			,   adapter_reduction_factor=getattr(xmod.cfg.model			,   '''bottleneck'''			,   2	)			,   adapter_layer_norm=xmod.cfg.model.adapter_layer_norm			,   adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm			,   ln_before_adapter=xmod.cfg.model.ln_before_adapter			,   languages=xmod.cfg.model.languages			,   )
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

	print('''Our X-MOD config:'''			,   _lowercase	)

	lowerCAmelCase_      							=			XmodForSequenceClassification(_lowercase	) if classification_head else XmodForMaskedLM(_lowercase	)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_tokens.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_positions.weight
	lowerCAmelCase_      							=			torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers	):
		# Encoder: start of layer
		lowerCAmelCase_      							=			model.roberta.encoder.layer[i]
		lowerCAmelCase_      							=			xmod_sent_encoder.layers[i]

		# self attention
		lowerCAmelCase_      							=			layer.attention.self
		if not (
		    xmod_layer.self_attn.k_proj.weight.data.shape
		    == xmod_layer.self_attn.q_proj.weight.data.shape
		    == xmod_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)	)
		):
			raise AssertionError('''Dimensions of self-attention weights do not match.'''	)

		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.bias

		# self-attention output
		lowerCAmelCase_      							=			layer.attention.output
		if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
			raise AssertionError('''Dimensions of self-attention output weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.bias

		# intermediate
		lowerCAmelCase_      							=			layer.intermediate
		if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of intermediate weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias

		# output
		lowerCAmelCase_      							=			layer.output
		if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of feed-forward weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.bias
		if bert_output.adapter_layer_norm is not None:
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.weight
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.bias

		if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
			raise AssertionError('''Lists of language adapters do not match.'''	)
		for lang_code, adapter in xmod_layer.adapter_modules.items():
			lowerCAmelCase_      							=			bert_output.adapter_modules[lang_code]
			lowerCAmelCase_      							=			xmod_layer.adapter_modules[lang_code]
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.weight
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.bias

	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.bias
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.bias
	else:
		# LM Head
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	lowerCAmelCase_      							=			xmod.encode(_lowercase	).unsqueeze(0	)  # batch of size 1
	model.roberta.set_default_language(_lowercase	)

	lowerCAmelCase_      							=			model(_lowercase	)[0]
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''](xmod.extract_features(_lowercase	)	)
	else:
		lowerCAmelCase_      							=			xmod.model(_lowercase			,   lang_id=[SAMPLE_LANGUAGE]	)[0]
	print(our_output.shape			,   their_output.shape	)
	lowerCAmelCase_      							=			torch.max(torch.abs(our_output - their_output	)	).item()
	print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
	lowerCAmelCase_      							=			torch.allclose(_lowercase			,   _lowercase			,   atol=1E-3	)
	print('''Do both models output the same tensors?'''			,   '''🔥''' if success else '''💩'''	)
	if not success:
		raise Exception('''Something went wRoNg'''	)

	Path(_lowercase	).mkdir(parents=_lowercase			,   exist_ok=_lowercase	)
	print(F"""Saving model to {pytorch_dump_folder_path}"""	)
	model.save_pretrained(_lowercase	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
		)
		lowercase_							=							parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)